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Towards a Reuse Strategic Decision Pattern Framework – from Theories to Practices

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Abstract

This paper demonstrates our proposed Reuse Strategic Decision Pattern Framework (RSDPF) based on blending ANP and TOPSIS techniques, enabled by the OSM model with data analytics. The motivation, related work, theory, the use and deployment, and the service deployment of the framework have been discussed in details. In this paper, RSDPF framework is demonstrated by the data analysis and interpretations based on a financial service firm. The OSM model allows 3 step of processed to be performed in one go to perform statistical tests, identify linear relations, check consistency on dataset and calculate OLS regression. The aim is to identify the actual, expected and risk rates of profitability. Code and services can be reused to compute for analysis. Service integration of the RSDPF framework has been demonstrated. Results confirm that there is a high extent of reliability. In this paper, we have demonstrated the reuse and integration of the framework supported by the case study of the financial service firm with its data analysis and service to justify our research contributions – reuse and integration in statistical data mining, knowledge and heuristic discovery and finally domain transference.

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Authors and Affiliations

  1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Victor Chang

  2. Research Institute of Big Data Analytics, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Victor Chang

  3. Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt

    Mohamed Abdel-Basset

  4. Leeds Beckett University, Leeds, UK

    Muthu Ramachandran

Authors
  1. Victor Chang
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  2. Mohamed Abdel-Basset
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  3. Muthu Ramachandran
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Correspondence to Victor Chang.

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Chang, V., Abdel-Basset, M. & Ramachandran, M. Towards a Reuse Strategic Decision Pattern Framework – from Theories to Practices. Inf Syst Front 21, 27–44 (2019). https://doi.org/10.1007/s10796-018-9853-8

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  • DOI: https://doi.org/10.1007/s10796-018-9853-8

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